
# First I have imported the libraries and data
import numpy as np
import pandas as pd
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objs as go
from pandas.plotting import parallel_coordinates
from plotly.subplots import make_subplots

df = pd.read_csv("players_list.csv")
df.head()


# Some exploration into the data

df.shape

(680, 42)


df.describe()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 680 entries, 0 to 679
Data columns (total 42 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                680 non-null    int64  
 1   Player                    680 non-null    object 
 2   Match_played              680 non-null    int64  
 3   Starts                    680 non-null    int64  
 4   Minutes_played            680 non-null    int64  
 5   Gls                       680 non-null    int64  
 6   Ast                       680 non-null    int64  
 7   Shots                     680 non-null    int64  
 8   shots_on_target           680 non-null    int64  
 9   goals_per_shot            431 non-null    float64
 10  goals_per_shot_on_target  247 non-null    float64
 11  non_penalty_goals         680 non-null    int64  
 12  penalty_goal              680 non-null    int64  
 13  CrdY                      680 non-null    int64  
 14  CrdR                      680 non-null    int64  
 15  pass_comp                 677 non-null    float64
 16  pass_attempt              677 non-null    float64
 17  pass_comp%                674 non-null    float64
 18  tackles_made              677 non-null    float64
 19  successful_tackles        680 non-null    int64  
 20  Blocks                    677 non-null    float64
 21  interceptions             680 non-null    int64  
 22  clearence                 677 non-null    float64
 23  Err                       677 non-null    float64
 24  Touches                   677 non-null    float64
 25  successful_dribbles       677 non-null    float64
 26  dribbles_attempted        677 non-null    float64
 27  successful_dribbles%      435 non-null    float64
 28  fouls_commited            680 non-null    int64  
 29  fouls_drawn               680 non-null    int64  
 30  offside                   680 non-null    int64  
 31  crosses                   680 non-null    int64  
 32  penalty_kick_won          677 non-null    float64
 33  penalty_kick_conceded     677 non-null    float64
 34  own_goal                  680 non-null    int64  
 35  aerial_duo_won            677 non-null    float64
 36  aerial_duo_lost           677 non-null    float64
 37  Pos                       680 non-null    object 
 38  Age                       680 non-null    int64  
 39  Club_country              679 non-null    object 
 40  Club                      679 non-null    object 
 41  Team                      680 non-null    object 
dtypes: float64(17), int64(20), object(5)
memory usage: 223.2+ KB


df['Team'].unique()

array(['Argentina', 'France', 'Croatia', 'Morocco', 'Netherlands',
       'England', 'Brazil', 'Portugal', 'Japan', 'Senegal', 'Australia',
       'Switzerland', 'Spain', 'United States', 'Poland',
       'Korea Republic', 'Germany', 'Ecuador', 'Cameroon', 'Uruguay',
       'Tunisia', 'Mexico', 'Belgium', 'Ghana', 'Saudi Arabia', 'IR Iran',
       'Costa Rica', 'Denmark', 'Serbia', 'Wales', 'Canada', 'Qatar'],
      dtype=object)


number_of_teams = len(df['Team'].unique())
print("The total numbers of country which took part in the FIFA 2022 world cup are:", number_of_teams)

The total numbers of country which took part in the FIFA 2022 world cup are: 32


df.isnull().sum()

Unnamed: 0                    0
Player                        0
Match_played                  0
Starts                        0
Minutes_played                0
Gls                           0
Ast                           0
Shots                         0
shots_on_target               0
goals_per_shot              249
goals_per_shot_on_target    433
non_penalty_goals             0
penalty_goal                  0
CrdY                          0
CrdR                          0
pass_comp                     3
pass_attempt                  3
pass_comp%                    6
tackles_made                  3
successful_tackles            0
Blocks                        3
interceptions                 0
clearence                     3
Err                           3
Touches                       3
successful_dribbles           3
dribbles_attempted            3
successful_dribbles%        245
fouls_commited                0
fouls_drawn                   0
offside                       0
crosses                       0
penalty_kick_won              3
penalty_kick_conceded         3
own_goal                      0
aerial_duo_won                3
aerial_duo_lost               3
Pos                           0
Age                           0
Club_country                  1
Club                          1
Team                          0
dtype: int64


# Let's explore some intitial correalation between features
sns.heatmap(df.corr())

<AxesSubplot:>


df.corr()


# Let's find out which players have scored most goals
def visualize_top_players(df, metric, position, title, ylabel, save_as=None):
    plt.figure(figsize=(12, 6))
    top_10_players = df[df['Pos'] == position].nlargest(10, metric).sort_values(metric, ascending=False)
    sns.barplot(x=metric, y='Player', data=top_10_players, palette='viridis')
    plt.title(title)
    plt.xlabel(ylabel)
    plt.ylabel('Player')
    plt.tight_layout()
    if save_as:
        plt.savefig(save_as, dpi=300)
    plt.show()
visualize_top_players(df, 'Gls', 'FW', 'Top 10 Forwards by Goals Scored', 'Goals Scored')


# We want to find out players based on "Goals", "Assists", "Shots", "Passes Completed", "Touches", "Successful Dribbles"
# First selecting top 10 players based on the required attributes
top_players = df.nlargest(10, ["Gls", "Ast", "Shots", "pass_comp", "Touches", "successful_dribbles"])

# Normalized the data
normalized_top_players = top_players[["Gls", "Ast", "Shots", "pass_comp", "Touches", "successful_dribbles"]].apply(lambda x: x / x.max(), axis=0)

# Created a radar chart as below:

attributes = ["Goals", "Assists", "Shots", "Passes Completed", "Touches", "Successful Dribbles"]
num_vars = len(attributes)
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
angles += angles[:1]
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
plt.xticks(angles[:-1], attributes, color="grey", size=12)
ax.set_rlabel_position(30)
plt.yticks([0.25, 0.5, 0.75, 1], ["25%", "50%", "75%", "100%"], color="grey", size=10)
plt.ylim(0, 1)

for index, row in normalized_top_players.iterrows():
    stats = row.tolist()
    stats += stats[:1]
    ax.plot(angles, stats, linewidth=2, label=top_players.loc[index, "Player"])
    ax.fill(angles, stats, alpha=0.25)

plt.legend(loc="center left", bbox_to_anchor=(1.15, 0.5), ncol=1)
plt.title("Top 10 Players")
plt.show()


top_players = top_players.reset_index()
top_players = top_players.drop(columns = 'index')
top_players = top_players.drop(columns = 'Unnamed: 0')


top_players = top_players[["Player", "Gls", "Ast", "Shots", "pass_comp", "Touches", "successful_dribbles"]]


top_players


# Now I would like to find out the contribution of Lionel Messi to Team Argentina based on the same metrics taken above

df_messi = df[(df['Player'] == 'Lionel Messi') & (df['Team'] == 'Argentina')]
metrics = ['Gls', 'Ast', 'Shots', 'pass_comp', 'Touches', 'successful_dribbles']
values = df_messi[metrics].values[0]

# Calculated the overall performance of Argentina's team in the selected metrics
df_team = df[df['Team'] == 'Argentina']
total_values = df_team[metrics].sum().values
max_values = df[metrics].max().values

# Created the radar chart as below:
fig = go.Figure()

fig.add_trace(go.Scatterpolar(
    r=total_values,
    theta=metrics,
    fill='toself',
    name='Argentina',
    line_color='blue'
))

fig.add_trace(go.Scatterpolar(
    r=values,
    theta=metrics,
    fill='toself',
    name='Lionel Messi',
    line_color='red'
))

fig.update_layout(
    polar=dict(
        radialaxis=dict(
            visible=True,
            range=[0, max_values.max()]
        )
    ),
    showlegend=True,
    title={
        'text': 'Lionel Messi Contribution to Argentina Team Performance',
        'y': 0.95,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    margin=dict(l=50, r=50, t=100, b=50)
)

fig.show()


# And now I would like to find out the contribution of Kylian Mbappe to Team France

df_mbappe = df[(df['Player'] == 'Kylian Mbappé') & (df['Team'] == 'France')]
metrics = ['Gls', 'Ast', 'Shots', 'pass_comp', 'Touches', 'successful_dribbles']
values = df_mbappe[metrics].values[0]

# Calculated the overall performance of France's team in the selected metrics
df_team = df[df['Team'] == 'France']
total_values = df_team[metrics].sum().values
max_values = df[metrics].max().values

# Created the radar chart as below:

fig = go.Figure()

fig.add_trace(go.Scatterpolar(
    r=total_values,
    theta=metrics,
    fill='toself',
    name='France',
    line_color='blue'
))

fig.add_trace(go.Scatterpolar(
    r=values,
    theta=metrics,
    fill='toself',
    name='Kylian Mbappé',
    line_color='red'
))

fig.update_layout(
    polar=dict(
        radialaxis=dict(
            visible=True,
            range=[0, max_values.max()]
        )
    ),
    showlegend=True,
    title={
        'text': 'Kylian Mbappé Contribution to France Team Performance',
        'y': 0.95,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    margin=dict(l=50, r=50, t=100, b=50)
)

fig.show()


# Now I want to find the the top 10 midfield players of the FIFA World cup 2022 

midfielders = df[df['Pos'] == 'MF']

# Selected the top 10 midfielders based on the sum of the chosen attributes
top_midfielders = midfielders.nlargest(10, ['Ast', 'pass_comp', 'successful_tackles', 'interceptions', 'Touches', 'crosses', 'aerial_duo_won'], keep='first')
columns = ['Player', 'Ast', 'pass_comp', 'successful_tackles', 'interceptions', 'Touches', 'crosses', 'aerial_duo_won']
top_midfielders = top_midfielders[columns]

# Created a custom normalization function
def custom_normalize(df):
    result = df.copy()
    for column in df.columns:
        if column != 'Player':
            min_val = df[column].min()
            result[column] = df[column] - min_val
    return result

# Normalized the data with the custom function
normalized_top_midfielders = custom_normalize(top_midfielders)

# Created the parallel coordinates plot in below:
fig, ax = plt.subplots(figsize=(15, 10))
parallel_coordinates(normalized_top_midfielders, 'Player', ax=ax, colormap='Set1')

# Customized the plot
ax.set_xticklabels(columns[1:])
ax.set_title('Top 10 Midfielders')
ax.legend().set_title('Player Names')
y_tick_interval = 20
y_min = np.floor(normalized_top_midfielders.iloc[:, 1:].min().min() / y_tick_interval) * y_tick_interval
y_max = np.ceil(normalized_top_midfielders.iloc[:, 1:].max().max() / y_tick_interval) * y_tick_interval
ax.set_ylim(y_min, y_max)
ax.yaxis.set_ticks(np.arange(y_min, y_max + y_tick_interval, y_tick_interval))

plt.show()


top_midfielders = top_midfielders.reset_index()
top_midfielders = top_midfielders.drop(columns = 'index')


top_midfielders


# Now let's find out Top 10 defenders based on their defensive attributes
defenders = df[df['Pos'] == 'DF']
top_defenders = defenders.nlargest(10, ['successful_tackles', 'interceptions', 'Blocks', 'clearence', 'aerial_duo_won'], keep='all')

# Created a grouped bar chart below:
labels = top_defenders['Player']
x = np.arange(len(labels))
width = 0.15

fig, ax = plt.subplots(figsize=(15, 8))
rects1 = ax.bar(x - 2 * width, top_defenders['successful_tackles'], width, label='Successful Tackles')
rects2 = ax.bar(x - width, top_defenders['interceptions'], width, label='Interceptions')
rects3 = ax.bar(x, top_defenders['Blocks'], width, label='Blocks')
rects4 = ax.bar(x + width, top_defenders['clearence'], width, label='Clearance')
rects5 = ax.bar(x + 2 * width, top_defenders['aerial_duo_won'], width, label='Aerial Duels Won')

ax.set_ylabel('Count')
ax.set_title('Top 10 Defenders in the World Cup')
ax.set_xticks(x)
ax.set_xticklabels(labels, rotation=45, ha='right')
ax.legend()

plt.show()


top_defenders = top_defenders.drop(columns = 'Unnamed: 0')
top_defenders = top_defenders.reset_index()
top_defenders = top_defenders.drop(columns = 'index')


top_defenders = top_defenders[["Player", "successful_tackles", "interceptions", "Blocks", "clearence", "aerial_duo_won"]]
top_defenders


# Let's find out top 10 Goalkeepers
goalkeepers = df[df['Pos'] == 'GK']
top_goalkeepers = goalkeepers.nlargest(10, ['Minutes_played', 'pass_comp%', 'Err', 'aerial_duo_won', 'CrdY', 'CrdR'], keep='all')

# Melted the data frame to a long format for pointplot
melted_data = pd.melt(top_goalkeepers, id_vars=['Player'], value_vars=['Minutes_played', 'Err', 'aerial_duo_won', 'CrdY', 'CrdR', 'pass_comp%'])

# Created a pointplot below:

plt.figure(figsize=(12, 6))
sns.set(style="whitegrid")
ax = sns.pointplot(data=melted_data, x='Player', y='value', hue='variable', palette="deep", linestyles="--", dodge=True)

for line, name in zip(ax.lines, melted_data['variable'].unique()):
    y = line.get_ydata()
    ax.fill_between(x=line.get_xdata(), y1=y - np.std(y), y2=y + np.std(y), alpha=0.2)

ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
ax.set_ylabel('Attribute values')
ax.set_xlabel('Goalkeepers')
ax.set_title('Top 10 Goalkeepers in the World Cup')

def onclick(event):
    if event.inaxes == ax:
        for point in ax.collections:
            if point.contains(event)[0]:
                index = point.get_offsets()[point.contains(event)[1]]['ind'][0]
                x = point.get_offsets()[0][0]
                y = point.get_offsets()[0][1]
                player = ax.get_xticklabels()[int(x)].get_text()
                attribute = melted_data['variable'].unique()[point.get_array().index(point.get_sizes()[index])]
                value = y
                print(f"Player: {player}, Attribute: {attribute}, Value: {value}")

plt.gcf().canvas.mpl_connect('button_press_event', onclick)

plt.show()


# Created a box plot:
plt.figure(figsize = (12,12))
sns.boxplot(x = 'Age', y = 'Team', data = df)
plt.xticks(rotation = 90)

(array([15., 20., 25., 30., 35., 40., 45.]),
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


# Now I wanted to group the data by "Team" and "Age", and count the number of players in each group
age_distribution = df.groupby(['Team', 'Age'])['Player'].count().reset_index(name='Count')

# Created a bar chart of the age distribution for each team
fig, ax = plt.subplots(figsize=(10, 6))
for team in age_distribution['Team'].unique():
    team_data = age_distribution[age_distribution['Team'] == team]
    ax.bar(team_data['Age'], team_data['Count'], label=team)

ax.set_title('Age Distribution of Players in Different Teams')
ax.set_xlabel('Age')
ax.set_ylabel('Number of Players')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

plt.show()


age_distribution


# Finding out the top 10 countries with the most players in the youngest age group
age_younger = age_distribution[age_distribution['Age'] == age_distribution['Age'].min()].sort_values('Count', ascending=False).head(10)
age_younger


# Grouped the data by "Team" and calculate the mean age for each group
team_mean_age = df.groupby('Team')['Age'].mean()

# Sorted the mean age by ascending order and select the top 10 teams
top_10_youngest_teams = team_mean_age.sort_values().head(10)

print(top_10_youngest_teams)

Team
United States    24.100000
Ecuador          24.277778
Ghana            24.950000
Spain            25.523810
Senegal          25.800000
Morocco          25.840000
France           25.875000
Portugal         25.958333
Netherlands      26.047619
Wales            26.055556
Name: Age, dtype: float64


# Similarly finding out the oldest group
top_10_oldest_teams = team_mean_age.sort_values(ascending = False).head(10)
print(top_10_oldest_teams)

Team
IR Iran           28.047619
Belgium           27.950000
Mexico            27.666667
Uruguay           27.631579
Qatar             27.600000
Brazil            27.500000
Canada            27.368421
Korea Republic    27.333333
Croatia           27.238095
Saudi Arabia      27.130435
Name: Age, dtype: float64


youngest_teams_df = pd.DataFrame({'Team': top_10_youngest_teams.index, 'Age': top_10_youngest_teams.values})

youngest_teams_df


oldest_teams_df = pd.DataFrame({'Team': top_10_oldest_teams.index, 'Age': top_10_oldest_teams.values})
oldest_teams_df


# The total number of goals scored by the 10 youngest teams
youngest_teams_goals = df[df['Team'].isin(top_10_youngest_teams.index)].groupby('Team')['Gls'].sum().reset_index(name='Total Goals')

# The total number of goals scored by the 10 oldest teams is:
oldest_teams_goals = df[df['Team'].isin(top_10_oldest_teams.index)].groupby('Team')['Gls'].sum().reset_index(name='Total Goals')

# Now Calculating the total Shots by the top 10 youngest teams
youngest_teams_shots = df[df['Team'].isin(top_10_youngest_teams.index)].groupby('Team')['Shots'].sum().reset_index(name = 'Total Shots')

# Now Calculating the total Shots by the top 10 youngest teams
oldest_teams_shots = df[df['Team'].isin(top_10_oldest_teams.index)].groupby('Team')['Shots'].sum().reset_index(name = 'Total Shots')

# Created a 3x2 grid of subplots
fig = make_subplots(rows=3, cols=2, subplot_titles=('10 Youngest Teams by Mean Age', '10 Oldest Teams by Mean Age',
                                                     'Total Goals Scored by 10 Youngest Teams', 'Total Goals Scored by 10 Oldest Teams', 'Total Shots attemped by the top 10 youngest teams', 'Total Shots attemped by the top 10 oldest teams'))


fig.add_trace(px.bar(youngest_teams_df, x='Team', y='Age', title='10 Youngest Teams by Mean Age').data[0], row=1, col=1)
fig.update_xaxes(title_text='Team', row=1, col=1)
fig.update_yaxes(title_text='Mean Age', range=[20, 30], row=1, col=1) # set y-axis range to 20-30
fig.update_traces(hovertemplate='Team: %{x}<br>Mean Age: %{y}', selector=dict(type='bar'))
fig.update_layout(xaxis_tickangle=-45)


fig.add_trace(px.bar(oldest_teams_df, x='Team', y='Age', title='10 Oldest Teams by Mean Age').data[0], row=1, col=2)
fig.update_xaxes(title_text='Team', row=1, col=2)
fig.update_yaxes(title_text='Mean Age', range=[20, 30], row=1, col=2) # set y-axis range to 20-30
fig.update_traces(hovertemplate='Team: %{x}<br>Mean Age: %{y}', selector=dict(type='bar'))
fig.update_layout(xaxis_tickangle=-45)

fig.add_trace(px.scatter(youngest_teams_goals, x='Team', y='Total Goals', title='Total Goals Scored by 10 Youngest Teams').data[0], row=2, col=1)
fig.update_xaxes(title_text='Team', row=2, col=1)
fig.update_yaxes(title_text='Total Goals', row=2, col=1)
fig.update_traces(hovertemplate='Team: %{x}<br>Total Goals: %{y}', selector=dict(type='scatter'))

fig.add_trace(px.scatter(oldest_teams_goals, x='Team', y='Total Goals', title='Total Goals Scored by 10 Oldest Teams').data[0], row=2, col=2)
fig.update_xaxes(title_text='Team', row=2, col=2)
fig.update_yaxes(title_text='Total Goals', row=2, col=2)
fig.update_traces(hovertemplate='Team: %{x}<br>Total Goals: %{y}', selector=dict(type='scatter'))

fig.add_trace(px.scatter(youngest_teams_shots, x='Team', y='Total Shots', title='Total Shots attempted by 10 youngest Teams').data[0], row=3, col=1)
fig.update_xaxes(title_text='Team', row=3, col=1)
fig.update_yaxes(title_text='Total Shots Attempted', row=3, col=1)
fig.update_traces(hovertemplate='Team: %{x}<br>Total Shots: %{y}', selector=dict(type='scatter'))

fig.add_trace(px.scatter(oldest_teams_shots, x='Team', y='Total Shots', title='Total Shots attempted by 10 Oldest Teams').data[0], row=3, col=2)
fig.update_xaxes(title_text='Team', row=3, col=2)
fig.update_yaxes(title_text='Total Shots Attempted', row=3, col=2)
fig.update_traces(hovertemplate='Team: %{x}<br>Total Shots: %{y}', selector=dict(type='scatter'))

fig.update_layout(height=1000, width=1000, title='Subplots of Team Stats', showlegend=False)

fig.show()

	Unnamed: 0	Match_played	Starts	Minutes_played	Gls	Ast	Shots	shots_on_target	goals_per_shot	goals_per_shot_on_target	...	fouls_commited	fouls_drawn	offside	crosses	penalty_kick_won	penalty_kick_conceded	own_goal	aerial_duo_won	aerial_duo_lost	Age
count	680.000000	680.000000	680.000000	680.000000	680.000000	680.000000	680.000000	680.000000	431.000000	247.000000	...	680.000000	680.000000	680.000000	680.000000	677.000000	677.000000	680.000000	677.000000	677.000000	680.000000
mean	339.500000	2.933824	2.070588	191.194118	0.250000	0.177941	2.107353	0.705882	0.097448	0.302794	...	2.351471	2.238235	0.373529	3.201471	0.028065	0.033973	0.002941	2.553914	2.550960	26.586765
std	196.443376	1.520611	1.753856	147.774808	0.698726	0.487652	3.031781	1.337780	0.215148	0.386570	...	2.614393	2.891103	0.864215	5.562789	0.165281	0.181295	0.054193	3.339578	2.904162	4.161129
min	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	17.000000
25%	169.750000	2.000000	1.000000	68.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	24.000000
50%	339.500000	3.000000	2.000000	173.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	...	2.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	2.000000	26.000000
75%	509.250000	4.000000	3.000000	270.000000	0.000000	0.000000	3.000000	1.000000	0.095000	0.500000	...	3.000000	3.000000	0.000000	4.000000	0.000000	0.000000	0.000000	4.000000	4.000000	29.000000
max	679.000000	7.000000	7.000000	690.000000	8.000000	3.000000	29.000000	13.000000	1.000000	1.000000	...	17.000000	22.000000	7.000000	40.000000	1.000000	1.000000	1.000000	21.000000	17.000000	39.000000

	Unnamed: 0	Match_played	Starts	Minutes_played	Gls	Ast	Shots	shots_on_target	goals_per_shot	goals_per_shot_on_target	...	fouls_commited	fouls_drawn	offside	crosses	penalty_kick_won	penalty_kick_conceded	own_goal	aerial_duo_won	aerial_duo_lost	Age
Unnamed: 0	1.000000	-0.490549	-0.309573	-0.368539	-0.202565	-0.225942	-0.242663	-0.232980	-0.126007	-0.122046	...	-0.250052	-0.238431	-0.122613	-0.146816	-0.094811	-0.064572	-0.082036	-0.174930	-0.195327	0.050901
Match_played	-0.490549	1.000000	0.778739	0.848660	0.326088	0.339639	0.486162	0.403811	0.116687	0.105231	...	0.529319	0.511456	0.309100	0.352233	0.142118	0.157687	0.091725	0.418102	0.469373	0.057818
Starts	-0.309573	0.778739	1.000000	0.965453	0.269201	0.309023	0.415141	0.334010	0.076403	0.078123	...	0.520052	0.509324	0.261445	0.373811	0.130318	0.191804	0.075288	0.486916	0.448455	0.147282
Minutes_played	-0.368539	0.848660	0.965453	1.000000	0.280319	0.328026	0.429074	0.342593	0.069960	0.086830	...	0.546712	0.520005	0.250981	0.375959	0.126929	0.203570	0.098317	0.506957	0.463340	0.136834
Gls	-0.202565	0.326088	0.269201	0.280319	1.000000	0.314446	0.692270	0.792513	0.526255	0.556852	...	0.227555	0.340104	0.498764	0.207545	0.322541	-0.020694	0.019447	0.159343	0.313837	0.005192
Ast	-0.225942	0.339639	0.309023	0.328026	0.314446	1.000000	0.413410	0.360277	0.082697	0.082227	...	0.207322	0.312522	0.226461	0.445524	0.085024	0.048916	0.035896	0.169448	0.240142	0.037016
Shots	-0.242663	0.486162	0.415141	0.429074	0.692270	0.413410	1.000000	0.858581	0.044147	-0.024020	...	0.344178	0.497450	0.568691	0.388710	0.244262	-0.017590	0.042894	0.242734	0.410471	0.002004
shots_on_target	-0.232980	0.403811	0.334010	0.342593	0.792513	0.360277	0.858581	1.000000	0.260415	0.051345	...	0.284780	0.456810	0.524458	0.342233	0.258002	-0.043891	0.032264	0.163289	0.348779	-0.023982
goals_per_shot	-0.126007	0.116687	0.076403	0.069960	0.526255	0.082697	0.044147	0.260415	1.000000	0.853405	...	0.071507	0.095503	0.107968	-0.030793	0.056041	0.017345	-0.010310	0.079189	0.135264	-0.020073
goals_per_shot_on_target	-0.122046	0.105231	0.078123	0.086830	0.556852	0.082227	-0.024020	0.051345	0.853405	1.000000	...	0.039211	-0.005219	0.052264	-0.072580	0.022076	0.079855	0.004496	0.119314	0.117469	0.029050
non_penalty_goals	-0.205807	0.326183	0.253526	0.261712	0.957893	0.260537	0.635841	0.761241	0.608908	0.646610	...	0.221725	0.267833	0.470926	0.178294	0.248661	-0.016256	0.024905	0.168142	0.303858	-0.026405
penalty_goal	-0.084916	0.151189	0.169368	0.182824	0.583556	0.298520	0.481133	0.456541	0.016825	0.027556	...	0.122181	0.362369	0.310391	0.179130	0.358692	-0.022159	-0.006403	0.049122	0.173970	0.091776
CrdY	-0.015626	0.195284	0.231851	0.240299	-0.001865	0.036258	0.043610	-0.001031	-0.017840	0.027346	...	0.395270	0.187620	-0.025381	0.024578	0.058323	0.163839	0.016123	0.261886	0.140532	0.063494
CrdR	-0.013421	0.003350	-0.003098	0.012401	0.055086	0.090304	0.003622	0.031310	0.134753	0.072968	...	0.122152	0.000313	0.055803	-0.016626	-0.013100	0.091932	-0.004178	0.021857	0.031853	0.040019
pass_comp	-0.337401	0.637517	0.748612	0.794798	0.068449	0.227109	0.212047	0.141490	-0.049060	-0.016957	...	0.471239	0.385063	0.009464	0.272480	0.047728	0.193699	0.116189	0.472333	0.285711	0.102591
pass_attempt	-0.341138	0.675461	0.791300	0.837237	0.092865	0.259407	0.246132	0.171911	-0.037637	-0.009599	...	0.497775	0.416231	0.037927	0.333904	0.058868	0.196556	0.113158	0.488611	0.312131	0.113512
pass_comp%	-0.130021	0.085859	0.151147	0.161234	-0.091741	-0.008618	-0.060513	-0.092056	-0.109049	-0.067914	...	0.062353	0.021789	-0.132147	-0.108529	-0.039181	0.055401	0.044067	0.051754	-0.075159	-0.014793
tackles_made	-0.257068	0.543468	0.569544	0.594768	0.041464	0.214729	0.185477	0.134276	0.007901	-0.024909	...	0.537500	0.453978	0.023657	0.333281	0.062889	0.100473	0.152503	0.328125	0.262341	-0.034588
successful_tackles	-0.248619	0.513188	0.521488	0.549360	0.040102	0.220294	0.165689	0.126689	0.008717	-0.024014	...	0.504422	0.424772	-0.005760	0.290894	0.063049	0.099269	0.157435	0.286430	0.249880	-0.049538
Blocks	-0.217767	0.524557	0.574308	0.590212	0.138760	0.236160	0.271825	0.209146	0.070606	0.091623	...	0.496361	0.377945	0.120288	0.311087	0.097089	0.045647	0.066120	0.417858	0.404824	-0.052795
interceptions	-0.203893	0.461795	0.526662	0.556883	-0.010884	0.172832	0.105343	0.037981	0.022267	0.030630	...	0.483546	0.306065	-0.004548	0.213401	0.009063	0.163685	0.050412	0.393334	0.253199	0.023618
clearence	-0.154188	0.397278	0.513323	0.544387	-0.020366	0.082782	-0.013595	-0.062684	0.053994	0.152817	...	0.320596	0.102569	-0.040836	0.026026	-0.027512	0.137042	0.064533	0.552747	0.313761	0.097433
Err	0.030215	0.074056	0.142806	0.150960	-0.027723	-0.027436	-0.026400	-0.035104	0.015221	0.042957	...	-0.016883	-0.034164	0.001924	-0.032978	-0.005918	0.047486	-0.012502	0.012676	-0.041721	0.086336
Touches	-0.351865	0.715367	0.823268	0.869127	0.140083	0.290248	0.307929	0.226339	-0.020882	-0.000032	...	0.540109	0.465694	0.083339	0.367966	0.079906	0.193219	0.112844	0.510811	0.359277	0.098973
successful_dribbles	-0.168322	0.382079	0.356748	0.365697	0.413883	0.340259	0.572614	0.479053	0.001031	-0.044802	...	0.271066	0.503152	0.387890	0.443577	0.192038	-0.018780	0.010131	0.069986	0.192382	-0.068166
dribbles_attempted	-0.176865	0.432040	0.388740	0.402446	0.445515	0.379736	0.631225	0.555133	0.022006	-0.054264	...	0.336980	0.602395	0.442160	0.531044	0.216382	-0.021852	0.000903	0.062195	0.249767	-0.111055
successful_dribbles%	0.024714	-0.020488	0.044061	0.031018	-0.034576	-0.023019	-0.019713	-0.061044	-0.053311	-0.010854	...	-0.038616	-0.091477	-0.066925	-0.051810	-0.042086	0.004206	0.015685	0.063424	-0.040089	0.165479
fouls_commited	-0.250052	0.529319	0.520052	0.546712	0.227555	0.207322	0.344178	0.284780	0.071507	0.039211	...	1.000000	0.451670	0.193417	0.285152	0.188675	0.183119	0.044667	0.393174	0.435137	-0.037667
fouls_drawn	-0.238431	0.511456	0.509324	0.520005	0.340104	0.312522	0.497450	0.456810	0.095503	-0.005219	...	0.451670	1.000000	0.286759	0.401862	0.273809	0.043732	0.014321	0.261862	0.412019	-0.014820
offside	-0.122613	0.309100	0.261445	0.250981	0.498764	0.226461	0.568691	0.524458	0.107968	0.052264	...	0.193417	0.286759	1.000000	0.282094	0.267811	-0.015039	0.007954	0.167785	0.274592	0.029881
crosses	-0.146816	0.352233	0.373811	0.375959	0.207545	0.445524	0.388710	0.342233	-0.030793	-0.072580	...	0.285152	0.401862	0.282094	1.000000	0.131982	-0.018590	-0.021510	0.039812	0.169380	0.016454
penalty_kick_won	-0.094811	0.142118	0.130318	0.126929	0.322541	0.085024	0.244262	0.258002	0.056041	0.022076	...	0.188675	0.273809	0.267811	0.131982	1.000000	-0.031867	-0.009250	0.097756	0.180386	0.058036
penalty_kick_conceded	-0.064572	0.157687	0.191804	0.203570	-0.020694	0.048916	-0.017590	-0.043891	0.017345	0.079855	...	0.183119	0.043732	-0.015039	-0.018590	-0.031867	1.000000	-0.010208	0.142347	0.073971	0.040507
own_goal	-0.082036	0.091725	0.075288	0.098317	0.019447	0.035896	0.042894	0.032264	-0.010310	0.004496	...	0.044667	0.014321	0.007954	-0.021510	-0.009250	-0.010208	1.000000	0.072522	-0.000956	-0.040319
aerial_duo_won	-0.174930	0.418102	0.486916	0.506957	0.159343	0.169448	0.242734	0.163289	0.079189	0.119314	...	0.393174	0.261862	0.167785	0.039812	0.097756	0.142347	0.072522	1.000000	0.617175	0.133261
aerial_duo_lost	-0.195327	0.469373	0.448455	0.463340	0.313837	0.240142	0.410471	0.348779	0.135264	0.117469	...	0.435137	0.412019	0.274592	0.169380	0.180386	0.073971	-0.000956	0.617175	1.000000	0.062759
Age	0.050901	0.057818	0.147282	0.136834	0.005192	0.037016	0.002004	-0.023982	-0.020073	0.029050	...	-0.037667	-0.014820	0.029881	0.016454	0.058036	0.040507	-0.040319	0.133261	0.062759	1.000000

	Player	Gls	Ast	Shots	pass_comp	Touches	successful_dribbles
0	Kylian Mbappé	8	2	29	173.0	322.0	25.0
1	Lionel Messi	7	3	27	300.0	462.0	15.0
2	Olivier Giroud	4	0	16	48.0	111.0	0.0
3	Julián Álvarez	4	0	11	93.0	176.0	0.0
4	Álvaro Morata	3	1	8	28.0	60.0	1.0
5	Gonçalo Ramos	3	1	7	25.0	56.0	0.0
6	Marcus Rashford	3	0	11	49.0	92.0	5.0
7	Enner Valencia	3	0	8	40.0	99.0	2.0
8	Richarlison	3	0	8	40.0	99.0	0.0
9	Bukayo Saka	3	0	7	84.0	150.0	2.0

	Player	Ast	pass_comp	successful_tackles	interceptions	Touches	crosses	aerial_duo_won
0	Antoine Griezmann	3	258.0	8	8	384.0	40	5.0
1	Enzo Fernández	1	412.0	15	3	535.0	2	9.0
2	Frenkie de Jong	1	323.0	5	6	412.0	3	8.0
3	Adrien Rabiot	1	237.0	9	10	352.0	7	15.0
4	Jude Bellingham	1	235.0	15	5	324.0	4	6.0
5	Alexis Mac Allister	1	234.0	4	3	332.0	1	5.0
6	Lucas Paquetá	1	169.0	5	2	231.0	4	3.0
7	Andre-Frank Zambo Anguissa	1	113.0	0	5	158.0	2	4.0
8	Aïssa Laïdouni	1	99.0	3	4	157.0	3	3.0
9	Steven Berghuis	1	97.0	1	0	145.0	13	1.0

	Player	successful_tackles	interceptions	Blocks	clearence	aerial_duo_won
0	Achraf Hakimi	17	9	7.0	12.0	5.0
1	Theo Hernández	13	4	5.0	8.0	8.0
2	Daley Blind	9	5	6.0	4.0	5.0
3	Eduardo Camavinga	9	3	4.0	6.0	2.0
4	Kalidou Koulibaly	9	2	4.0	30.0	11.0
5	Noussair Mazraoui	9	1	6.0	16.0	1.0
6	Jules Koundé	8	5	7.0	8.0	5.0
7	Josip Juranović	8	3	5.0	15.0	2.0
8	Marcos Acuña	8	2	8.0	9.0	5.0
9	Nicolás Tagliafico	7	9	9.0	9.0	4.0

Let's See how many countries took part in FIFA Football World Cup 2022?¶

So We got the top 10 players according to the total goals, total shot, Assists, Completed passes, Touches and successful dribbles.¶

1) Amongst them, Kylian Mbappe is the player who had most goals, completed most dribbles successfully and had most shots amongst the players.¶

2) Lionel Messi had the 2nd highest goals, most assits, had the most passes completed and most touches on the ball amongst the players.¶

3) No other forward players went nearly to the performance of Kylian Mbappe or Lionel Messi in this case when considered the above attributes.¶

So I think FIFA awarded the Golden Ball to Lionel Messi when considering all of this facts and was a fair decision.¶

Successful tackles, Interceptions, Blocks, Clearance and Aerial duo won are taken as defenders attribute. I choose stacked bar plots here because it is simple and easy to interpret and yet powerful visualization.¶

Achraf Hakimi, Theo Hernandez and Daley Blind were some fantastic defenders in the world cup¶

	Unnamed: 0	Player	Match_played	Starts	Minutes_played	Gls	Ast	Shots	shots_on_target	goals_per_shot	...	penalty_kick_won	penalty_kick_conceded	aerial_duo_won	aerial_duo_lost	Pos	Age	Club_country	Club	Team
0	0	Emiliano Martínez	7	7	690	0	0	0	0	NaN	...	0.0	0.0	2.0	0.0	GK	29	eng	Aston Villa	Argentina
1	1	Lionel Messi	7	7	690	7	3	27	13	0.11	...	1.0	0.0	2.0	7.0	FW	34	fr	Paris S-G	Argentina
2	2	Nicolás Otamendi	7	7	690	0	1	1	0	0.00	...	0.0	1.0	21.0	13.0	DF	34	pt	Benfica	Argentina
3	3	Rodrigo De Paul	7	7	599	0	0	7	3	0.00	...	0.0	0.0	2.0	3.0	MF	28	es	Atlético Madrid	Argentina
4	4	Nahuel Molina	7	6	567	1	1	2	1	0.50	...	0.0	0.0	0.0	6.0	DF	24	es	Atlético Madrid	Argentina

	Team	Age
0	United States	24.100000
1	Ecuador	24.277778
2	Ghana	24.950000
3	Spain	25.523810
4	Senegal	25.800000
5	Morocco	25.840000
6	France	25.875000
7	Portugal	25.958333
8	Netherlands	26.047619
9	Wales	26.055556

	Team	Age
0	IR Iran	28.047619
1	Belgium	27.950000
2	Mexico	27.666667
3	Uruguay	27.631579
4	Qatar	27.600000
5	Brazil	27.500000
6	Canada	27.368421
7	Korea Republic	27.333333
8	Croatia	27.238095
9	Saudi Arabia	27.130435

	Team	Age	Count
10	Australia	17	1
66	Costa Rica	17	1
141	Germany	17	1
299	Spain	17	1